Detail publikace

Degree of Parkinson’s Disease Severity Estimation Based on Speech Signal Processing

Originální název

Degree of Parkinson’s Disease Severity Estimation Based on Speech Signal Processing

Anglický název

Degree of Parkinson’s Disease Severity Estimation Based on Speech Signal Processing

Jazyk

en

Originální abstrakt

This paper deals with Parkinson’s disease (PD) severity estimation according to the Unified Parkinson’s Disease Rating Scale: motor subscale (UPDRS III), which quantifies the hallmark symptoms of PD, using an acoustic analysis of speech signals. Experimental dataset comprised 42 speech tasks acquired from 50 PD patients (UPDRS III ranged from 6 to 92). It was divided into subsets: words, sentences, reading text, monologue and diadochokinetic tasks. We performed a parametrization of the whole corpus and these groups separately using a wide range of conventional and novel speech features. We used guided regularized random forest algorithm to select features with maximum clinical information and performed random forests regression to estimate PD severity. According to significant correlations between true UPDRS III scores and scores predicted by the proposed methodology it was shown information extracted through variety of speech tasks can be used to estimate PD severity. of PD severity.

Anglický abstrakt

This paper deals with Parkinson’s disease (PD) severity estimation according to the Unified Parkinson’s Disease Rating Scale: motor subscale (UPDRS III), which quantifies the hallmark symptoms of PD, using an acoustic analysis of speech signals. Experimental dataset comprised 42 speech tasks acquired from 50 PD patients (UPDRS III ranged from 6 to 92). It was divided into subsets: words, sentences, reading text, monologue and diadochokinetic tasks. We performed a parametrization of the whole corpus and these groups separately using a wide range of conventional and novel speech features. We used guided regularized random forest algorithm to select features with maximum clinical information and performed random forests regression to estimate PD severity. According to significant correlations between true UPDRS III scores and scores predicted by the proposed methodology it was shown information extracted through variety of speech tasks can be used to estimate PD severity. of PD severity.

BibTex


@inproceedings{BUT126644,
  author="Zoltán {Galáž} and Jiří {Mekyska} and Zdeněk {Mžourek} and Tomáš {Kiska} and Zdeněk {Smékal} and Irena {Rektorová}",
  title="Degree of Parkinson’s Disease Severity Estimation Based on Speech Signal Processing",
  annote="This paper deals with Parkinson’s disease (PD) severity estimation according to the Unified Parkinson’s Disease
Rating Scale: motor subscale (UPDRS III), which quantifies the hallmark symptoms of PD, using an acoustic analysis of speech  signals. Experimental dataset comprised 42 speech tasks acquired from 50 PD patients (UPDRS III ranged from 6 to 92). It was divided into subsets: words, sentences, reading text, monologue and diadochokinetic tasks. We performed a parametrization of the whole corpus and these groups separately using a wide range of conventional and novel speech features. We used guided regularized random forest algorithm to select features with maximum clinical information and performed random forests regression to estimate PD severity. According to significant correlations between true UPDRS III scores and scores predicted by the proposed methodology it was shown information extracted through variety of speech tasks can be used to estimate PD severity.
of PD severity.",
  booktitle="Proceedings of the 39th International Conference on Telecommunication and Signal Processing, TSP 2016",
  chapter="126644",
  edition="1",
  howpublished="online",
  year="2016",
  month="june",
  pages="503--507",
  type="conference paper"
}